nep-mst New Economics Papers
on Market Microstructure
Issue of 2021‒08‒30
six papers chosen by
Thanos Verousis

  1. The currency that came in from the cold - Capital controls and the information content of order flow By Francis Breedon; Thórarinn G. Pétursson; Paolo Vitale
  2. Fragmentation, Price Formation, and Cross-Impact in Bitcoin Markets By Jakob Albers; Mihai Cucuringu; Sam Howison; Alexander Y. Shestopaloff
  3. Crypto Wash Trading By Lin William Cong; Xi Li; Ke Tang; Yang Yang
  4. Stock Price Level Effect By Borsboom, Charlotte; Füllbrunn, Sascha
  5. Non-parametric Estimation of Quadratic Hawkes Processes for Order Book Events By Antoine Fosset; Jean-Philippe Bouchaud; Michael Benzaquen
  6. A Stationary Kyle Setup: Microfounding propagator models By Michele Vodret; Iacopo Mastromatteo; Bence Tóth; Michael Benzaquen

  1. By: Francis Breedon; Thórarinn G. Pétursson; Paolo Vitale
    Abstract: We analyse how capital controls affect FX microstructure, using as a case study the introduction and subsequent removal of controls in Iceland. We use a VAR of private order flow, Central Bank order flow and EURISK that allows for contemporaneous feedback effects to analyse the impact and information content of trades and find that controls have profound effects. When controls were introduced, volume plummeted, the information content of trading activity declined and became less responsive to macro news. While there was no recovery of trading volume after controls were abolished, the information content and responsiveness of trading activity increased sharply.
    JEL: C32 F31 F32 G14 G15
    Date: 2021–06
  2. By: Jakob Albers; Mihai Cucuringu; Sam Howison; Alexander Y. Shestopaloff
    Abstract: In light of micro-scale inefficiencies induced by the high degree of fragmentation of the Bitcoin trading landscape, we utilize a granular data set comprised of orderbook and trades data from the most liquid Bitcoin markets, in order to understand the price formation process at sub-1 second time scales. To achieve this goal, we construct a set of features that encapsulate relevant microstructural information over short lookback windows. These features are subsequently leveraged first to generate a leader-lagger network that quantifies how markets impact one another, and then to train linear models capable of explaining between 10% and 37% of total variation in $500$ms future returns (depending on which market is the prediction target). The results are then compared with those of various PnL calculations that take trading realities, such as transaction costs, into account. The PnL calculations are based on natural $\textit{taker}$ strategies (meaning they employ market orders) that we associate to each model. Our findings emphasize the role of a market's fee regime in determining its propensity to being a leader or a lagger, as well as the profitability of our taker strategy. Taking our analysis further, we also derive a natural $\textit{maker}$ strategy (i.e., one that uses only passive limit orders), which, due to the difficulties associated with backtesting maker strategies, we test in a real-world live trading experiment, in which we turned over 1.5 million USD in notional volume. Lending additional confidence to our models, and by extension to the features they are based on, the results indicate a significant improvement over a naive benchmark strategy, which we also deploy in a live trading environment with real capital, for the sake of comparison.
    Date: 2021–08
  3. By: Lin William Cong; Xi Li; Ke Tang; Yang Yang
    Abstract: We introduce systematic tests exploiting robust statistical and behavioral patterns in trading to detect fake transactions on 29 cryptocurrency exchanges. Regulated exchanges feature patterns consistently observed in financial markets and nature; abnormal first-significant-digit distributions, size rounding, and transaction tail distributions on unregulated exchanges reveal rampant manipulations unlikely driven by strategy or exchange heterogeneity. We quantify the wash trading on each unregulated exchange, which averaged over 70% of the reported volume. We further document how these fabricated volumes (trillions of dollars annually) improve exchange ranking, temporarily distort prices, and relate to exchange characteristics (e.g., age and userbase), market conditions, and regulation.
    Date: 2021–08
  4. By: Borsboom, Charlotte; Füllbrunn, Sascha
    Abstract: Companies actively manipulate stock price ranges through IPOs, stock splits, and repurchases. Indeed, empirical results suggest that the stock's price range, whether at a high or low price level, affects market performance. Unfortunately, archival data does not allow us to test the effect of stock price levels on investor behavior due to uncontrolled confound effects. We thus conduct a controlled online experiment with 900 US retail investors to test whether a difference in stock price levels affects the investor's risk perception, the price forecast, and the investment. Even though we �nd no differences in risk perception and forecasts, our results show signi�cantly higher investments in high-priced stocks in comparison to low-priced stocks. This effect disappears when we allow fractional share purchases or restrict naive trading strategies.
    Keywords: stock price, nominal stock price puzzle, stock splits, number processing, fractional share purchases, naive trading strategies, numerosity
    JEL: C90 D14 G11
    Date: 2021–08–18
  5. By: Antoine Fosset; Jean-Philippe Bouchaud; Michael Benzaquen (LadHyX - Laboratoire d'hydrodynamique - X - École polytechnique - CNRS - Centre National de la Recherche Scientifique)
    Abstract: We propose an actionable calibration procedure for general Quadratic Hawkes models of order book events (market orders, limit orders, cancellations). One of the main features of such models is to encode not only the influence of past events on future events but also, crucially, the influence of past price changes on such events. We show that the empirically calibrated quadratic kernel is well described by a diagonal contribution (that captures past realised volatility), plus a rank-one "Zumbach" contribution (that captures the effect of past trends). We find that the Zumbach kernel is a power-law of time, as are all other feedback kernels. As in many previous studies, the rate of truly exogenous events is found to be a small fraction of the total event rate. These two features suggest that the system is close to a critical point -- in the sense that stronger feedback kernels would lead to instabilities.
    Date: 2021
  6. By: Michele Vodret; Iacopo Mastromatteo; Bence Tóth; Michael Benzaquen (LadHyX - Laboratoire d'hydrodynamique - X - École polytechnique - CNRS - Centre National de la Recherche Scientifique)
    Abstract: We provide an economically sound micro-foundation to linear price impact models, by deriving them as the equilibrium of a suitable agent-based system. Our setup generalizes the well-known Kyle model, by dropping the assumption of a terminal time at which fundamental information is revealed so to describe a stationary market, while retaining agents' rationality and asymmetric information. We investigate the stationary equilibrium for arbitrary Gaussian noise trades and fundamental information, and show that the setup is compatible with universal price diffusion at small times, and non-universal mean-reversion at time scales at which fluctuations in fundamentals decay. Our model provides a testable relation between volatility of prices, magnitude of fluctuations in fundamentals and level of volume traded in the market.
    Date: 2021

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